Robot-assisted and conventional therapies produce distinct rehabilitative trends in stroke survivors

Francisco J Valero-Cuevas, Verena Klamroth-Marganska, Carolee J Winstein, Robert Riener, Francisco J Valero-Cuevas, Verena Klamroth-Marganska, Carolee J Winstein, Robert Riener

Abstract

Background: Comparing the efficacy of alternative therapeutic strategies for the rehabilitation of motor function in chronically impaired individuals is often inconclusive. For example, a recent randomized clinical trial (RCT) compared robot-assisted vs. conventional therapy in 77 patients who had had chronic motor impairment after a cerebrovascular accident. While patients assigned to robotic therapy had greater improvements in the primary outcome measure (change in score on the upper extremity section of the Fugl-Meyer assessment), the absolute difference between therapies was small, which left the clinical relevance in question.

Methods: Here we revisit that study to test whether the multidimensional rehabilitative response of these patients can better distinguish between treatment outcomes. We used principal components analysis to find the correlation of changes across seven outcome measures between the start and end of 8 weeks of therapy. Permutation tests verified the robustness of the principal components found.

Results: Each therapy in fact produces different rehabilitative trends of recovery across the clinical, functional, and quality of life domains. A rehabilitative trend is a principal component that quantifies the correlations among changes in outcomes with each therapy.

Conclusions: These findings challenge the traditional emphasis of RCTs on using a single primary outcome measure to compare rehabilitative responses that are naturally multidimensional. This alternative approach to, and interpretation of, the results of RCTs may will lead to more effective therapies targeted for the multidimensional mechanisms of recovery.

Trial registration: ClinicalTrials.gov number NCT00719433 . Registered July 17, 2008.

Figures

Fig. 1
Fig. 1
Rehabilitative trends obtained from principal components analysis (PCA). Consider the schematic case of three outcomes, where the change in each outcome with therapy is plotted for all subjects. PCA finds the best linear fit to the data using 3 perpendicular vectors: the 1st, 2nd, and 3rd principal components (PCs), labeled in descending order by variance explained. Each PC is a rehabilitative trend that quantifies the correlations among outcomes
Fig. 2
Fig. 2
Graphical representation of the two most prominent rehabilitative trends for each therapy. The loadings of each trend quantify the correlations among changes in outcomes. Top: Shown as scaled arrows. Bottom: Shown as line plots

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Source: PubMed

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